CXL Review: Digital Analytics IX

Part 9 of a series of 12 posts that will document my progress, learnings, and discoveries from the Digital Analytics mini degree at CXL Institute.

Data presentation.

That is what this article will be about.

Charts. Trends, bars, columns, pies, and all that good stuff.

This will cover the first half of the CXL’s Google Data Studio course.

First of all, I got to say I’m really liking the instructor. She, besides knowing a bunch of stuff about data presentation, seems pretty passionate about it. Almost intoxicating. I didn´t even know I hated pie charts until she spoke very strongly about them. But hey, I learned that they are useful sometimes. Every single one has an occasion when it’s the best option. Every chart has it’s time to shine, we just got to find the right occasion.

So, let’s not hate on any charts. :)

Well, going into the course. I think it is important to first lay ground on the most important concepts. As I will talk solely about Google Data Studio, I’d like to be sure everyone is on the same page.

GDS is a totally free tool by Google to present data. This is the master-tool of data presentation. Remember when Mercer said that you had tools for capturing, storing, and presenting data? Well, this is the presenting part. Google Analytics is the storing and Google Tag Manager will be capturing. Just for the record.

As GDS presents data, that data must come from somewhere, right? There are a lot of data sources available to extract data from. A data source can be Facebook, Google Analytics, Linkedin, SemRush, Ahrefs, Google Sheets, or even Supermetrics.

This time, we’ll focus only on GA, because that’s what the course chose to use.

As in GA, in GDS we have dimensions and metrics. Letters and numbers.

And we will create reports based on that. Yay. That’s Google Data Studio in a nutshell. Here are some tips for the set up:

• Set data source at the report level
• PLAN! What data points will you need?
• How much engagement do you want in your report? Will it be flexible or static?
• Sketch it!
• What KPI’s will be more important? What type of charts will be better for them?
• Data / Pixel ratio. Huge game-changer. Let’s not waste pixels on things that don´t work and don´t serve any useful purpose.
• What you´ll be able to customize: page size, color palette, and layout.
• Available data visualizations include tables, scorecards, line charts, bar and column charts, maps, treemaps, scatter plots, pie, and donuts.

Let’s talk about what everyone wants to know. What type of chart should you use?

Line charts to showcase trends.

• Don’t use the grid, it will just make it hard to read and that would mean dead pixels.
• If you have multiple lines, use legends to differentiate.
• When to combine 2 metrics? When they tell a story together. Don´t just cramp multiple things together because it looks “good”. It must serve a purpose.

Bar and column charts to showcase differences.

• Overall, bars are easier to read. So use that. The text doesn´t have to be arranged diagonally and be all weird, it will just stay horizontal.

Area charts to showcase differences overtime.

Sparklines to showcase trends but simpler.

• Useful when you have a lot of data to show.
• Useful when combined with a scorecard.

In table bars to visualize a lot of data in little space.

• Show numbers and charts
• You will need a lot of formatting to make it cute and understandable.
• Use pixels correctly.
• The scroll is better than having pages.
• Make them transparent, no color!
• Add totals
• Add a header.

Scorecards to show important numbers.

• Useful for showcasing KPI’s.
• When possible, always compact the number.
• Use decimals only when necessary to understand or interpret the data.

Pie charts to visualize parts of a whole.

• Don´t use them when there are too many pieces. As much as 3.
• The data must be part of a whole.
• If you need to use 2 pie charts, you are doing it wrong. Use another type of chart instead, it will likely be more useful.
• Do not use it to show sources or countries. Maybe devices.

Maps to visualize geography?

• Use only when super relevant. Always ask yourself, is it necessary?

Formatting is a big part of reports. A huge part really. We are trying to make it pleasant to the eye. So, here are some tips on how to use color.

• Call out a data point by making it BOLD.
• Use your brand colors.
• Keep colors consistent. If green=good and red=bad once, they stay that way through the report.
• Color your dimensions value across your report. It will make it easier to read.
• Use conditional formatting.
• Trust your intuition.

Well, here we saw a few tips and tricks for making a nice USEFUL report on Google Data Studio. Next time, we will dig a little deeper and maybe start on other data analysis tools! See you next time.